title: ‘Data Preprocessing’ output: html_document: code_folding: ‘hide’ —
Raw data
Dataset downloaded from mgandal’s github repository.
Load and annotate data
# Load csvs
datExpr = read.csv('./../Data/RNAseq_ASD_datExpr.csv', row.names=1)
datMeta = read.csv('./../Data/RNAseq_ASD_datMeta.csv')
# 1. Group brain regions by lobes
# 2. Remove '/' from Batch variable: (It is recommended (but not required) to use only letters, numbers,
# and delimiters '_' or '.', in levels of factors as these are safe characters for column names in R
# 3. Transform Diagnosis into a factor variable
datMeta = datMeta %>% mutate(Brain_Region = as.factor(Region)) %>%
mutate(Brain_lobe = ifelse(Brain_Region %in% c('BA4_6', 'BA9', 'BA24', 'BA44_45'), 'Frontal',
ifelse(Brain_Region %in% c('BA3_1_2_5', 'BA7'), 'Parietal',
ifelse(Brain_Region %in% c('BA38', 'BA39_40', 'BA20_37', 'BA41_42_22'), 'Temporal',
'Occipital')))) %>%
mutate(Batch = as.factor(gsub('/', '.', RNAExtractionBatch)),
Diagnosis = factor(Diagnosis_, levels=c('CTL','ASD'))) %>%
dplyr::select(-Diagnosis_)
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# NCBI biotype annotation
NCBI_biotype = read.csv('./../../../NCBI/Data/gene_biotype_info.csv') %>%
rename(Ensembl_gene_identifier='ensembl_gene_id', type_of_gene='gene_biotype', Symbol='hgnc_symbol') %>%
mutate(gene_biotype = ifelse(gene_biotype=='protein-coding','protein_coding',gene_biotype))
rm(GO_annotations)Check sample composition
Data description taken from the dataset’s synapse entry: RNAseq data was generated from 88 postmortem cortex brain samples from subjects with ASD (53 samples from 24 subjects) and non-psychiatric controls (35 samples from 17 subjects), across four cortical regions encompassing all major cortical lobes – frontal, temporal, parietal, and occipital. Brain samples were obtained from the Harvard Brain Bank as part of the Autism Tissue Project (ATP).
cat(paste0('Dataset includes ', nrow(datExpr), ' genes from ', ncol(datExpr), ' samples belonging to ',
length(unique(datMeta$Subject_ID)), ' different subjects.'))## Dataset includes 63682 genes from 88 samples belonging to 41 different subjects.Counts distribution: More than half of the counts are zero and most of the counts are relatively low, but there are some very high outliers
count_distr = summary(melt(datExpr))[,2]
for(i in 1:6){
print(count_distr[i])
}##
## "Min. : 0 "
##
## "1st Qu.: 0 "
##
## "Median : 0 "
##
## "Mean : 564 "
##
## "3rd Qu.: 27 "
##
## "Max. :27183314 "rm(count_distr, i)Diagnosis distribution by Sample: There are more ASD samples than controls
table_info = datMeta %>% apply_labels(Diagnosis = 'Diagnosis', Brain_lobe = 'Brain Lobe', Batch = 'Batch', Sex = 'Gender')
cro(table_info$Diagnosis)| #Total | |
|---|---|
| Diagnosis | |
| CTL | 35 |
| ASD | 53 |
| #Total cases | 88 |
Diagnosis distribution by Subject: There are more ASD patients than controls
cro(table_info$Diagnosis[!duplicated(table_info$Subject_ID)])| #Total | |
|---|---|
| Diagnosis | |
| CTL | 17 |
| ASD | 24 |
| #Total cases | 41 |
Brain region distribution: All regions seem to be balanced
cro(table_info$Brain_lobe)| #Total | |
|---|---|
| Brain Lobe | |
| Frontal | 22 |
| Occipital | 23 |
| Parietal | 23 |
| Temporal | 20 |
| #Total cases | 88 |
Diagnosis and brain region seem to be balanced except for the frontal lobe, where there are more control samples than ASD ones
cro(table_info$Diagnosis, list(table_info$Brain_lobe,total()))| Brain Lobe | #Total | |||||
|---|---|---|---|---|---|---|
| Frontal | Occipital | Parietal | Temporal | |||
| Diagnosis | ||||||
| CTL | 13 | 8 | 8 | 6 | 35 | |
| ASD | 9 | 15 | 15 | 14 | 53 | |
| #Total cases | 22 | 23 | 23 | 20 | 88 | |
Sex distribution: There are many more Male samples than Female ones
cro(table_info$Sex)| #Total | |
|---|---|
| Gender | |
| F | 15 |
| M | 73 |
| #Total cases | 88 |
Diagnosis and Gender seem to be balanced
cro(table_info$Diagnosis, list(table_info$Sex, total()))| Gender | #Total | |||
|---|---|---|---|---|
| F | M | |||
| Diagnosis | ||||
| CTL | 6 | 29 | 35 | |
| ASD | 9 | 44 | 53 | |
| #Total cases | 15 | 73 | 88 | |
Age distribution: Subjects between 2 and 60 years old with a mean close to 30
summary(datMeta$Age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 17.00 28.00 29.74 41.75 60.00Annotate genes with BioMart information
I was originally running this with the feb2014 version of BioMart because that’s the one that Gandal used (and it finds all of the Ensembl IDs, which other versions don’t), but it has some outdated biotype annotations, to fix this I’ll obtain all the information except the biotype label from BioMart in the same way as it had been done before, and then I’ll add the most current biotype label using information from NCBI’s website and then from BioMart in the following way:
1.Use BioMart to run a query with the original feb2014 version using the Ensembl IDs as keys to obtain all the information except the biotype labels
- Annotate genes with Biotype labels:
2.1 Use the NCBI annotations downloaded from NCBI’s website and processed in NCBI/RMarkdowns/20_02_07_clean_data.html (there is information for only 26K genes, so some genes will remain unlabelled)
2.2 Use the current version (jan2020) to obtain the biotype annotations using the Ensembl ID as keys (some genes don’t return a match)
2.3 For the genes that didn’t return a match, use the current version (jan2020) to obtain the biotype annotations using the gene name as keys (17 genes return multiple labels)
2.4 For the genes that returned multiple labels, use the feb2014 version with the Ensembl IDs as keys
labels_source = data.frame(data.frame('source' = c('NCBI', 'BioMart2020_byID', 'BioMart2020_byGene', 'BioMart2014'),
'n_matches' = rep(0,4)))
########################################################################################
# 1. Query archive version
getinfo = c('ensembl_gene_id','external_gene_id','chromosome_name','start_position',
'end_position','strand')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
datGenes = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=rownames(datExpr), mart=mart) %>%
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cat(paste0('1. ', sum(is.na(datGenes$start_position)), '/', nrow(datGenes),
' Ensembl IDs weren\'t found in the feb2014 version of BioMart'))## 1. 0/63677 Ensembl IDs weren't found in the feb2014 version of BioMart########################################################################################
########################################################################################
# 2. Get Biotype Labels
cat('2. Add biotype information')## 2. Add biotype information########################################################################################
# 2.1 Add NCBI annotations
datGenes = datGenes %>% left_join(NCBI_biotype, by=c('ensembl_gene_id','hgnc_symbol'))
cat(paste0('2.1 ' , sum(is.na(datGenes$gene_biotype)), '/', nrow(datGenes),
' Ensembl IDs weren\'t found in the NCBI database'))## 2.1 42904/63677 Ensembl IDs weren't found in the NCBI databaselabels_source$n_matches[1] = sum(!is.na(datGenes$gene_biotype))
########################################################################################
# 2.2 Query current BioMart version for gene_biotype using Ensembl ID as key
getinfo = c('ensembl_gene_id','gene_biotype')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='jan2020.archive.ensembl.org')
datGenes_biotype = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), mart=mart,
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## [===============================] 100% eta: 0scat(paste0('2.2 ' , sum(is.na(datGenes$gene_biotype))-nrow(datGenes_biotype), '/', sum(is.na(datGenes$gene_biotype)),
' Ensembl IDs weren\'t found in the jan2020 version of BioMart when querying by Ensembl ID'))## 2.2 9099/42904 Ensembl IDs weren't found in the jan2020 version of BioMart when querying by Ensembl ID# Add new gene_biotype info to datGenes
datGenes = datGenes %>% left_join(datGenes_biotype, by='ensembl_gene_id') %>%
mutate(gene_biotype = coalesce(as.character(gene_biotype.x), gene_biotype.y)) %>%
dplyr::select(-gene_biotype.x, -gene_biotype.y)
labels_source$n_matches[2] = sum(!is.na(datGenes$gene_biotype)) - labels_source$n_matches[1]
########################################################################################
# 3. Query current BioMart version for gene_biotype using gene symbol as key
missing_genes = unique(datGenes$hgnc_symbol[is.na(datGenes$gene_biotype)])
getinfo = c('hgnc_symbol','gene_biotype')
datGenes_biotype_by_gene = getBM(attributes=getinfo, filters=c('hgnc_symbol'), mart=mart,
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' genes weren\'t found in the current BioMart version when querying by gene name'))## 2.3 5712/7866 genes weren't found in the current BioMart version when querying by gene namedups = unique(datGenes_biotype_by_gene$hgnc_symbol[duplicated(datGenes_biotype_by_gene$hgnc_symbol)])
cat(paste0(' ', length(dups), ' genes returned multiple labels (these won\'t be added)'))## 17 genes returned multiple labels (these won't be added)# Update information
datGenes_biotype_by_gene = datGenes_biotype_by_gene %>% filter(!hgnc_symbol %in% dups)
datGenes = datGenes %>% left_join(datGenes_biotype_by_gene, by='hgnc_symbol') %>%
mutate(gene_biotype = coalesce(gene_biotype.x, gene_biotype.y)) %>%
dplyr::select(-gene_biotype.x, -gene_biotype.y)
labels_source$n_matches[3] = sum(!is.na(datGenes$gene_biotype)) - sum(labels_source$n_matches)
########################################################################################
# 4. Query feb2014 BioMart version for the missing biotypes
missing_ensembl_ids = unique(datGenes$ensembl_gene_id[is.na(datGenes$gene_biotype)])
getinfo = c('ensembl_gene_id','gene_biotype')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
datGenes_biotype_archive = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=missing_ensembl_ids, mart=mart)## Batch submitting query [===>---------------------------] 14% eta: 1s
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' genes weren\'t found in the feb2014 BioMart version when querying by Ensembl ID'))## 2.4 0/6648 genes weren't found in the feb2014 BioMart version when querying by Ensembl ID# Update information
datGenes = datGenes %>% left_join(datGenes_biotype_archive, by='ensembl_gene_id') %>%
mutate(gene_biotype = coalesce(gene_biotype.x, gene_biotype.y)) %>%
dplyr::select(-gene_biotype.x, -gene_biotype.y)
labels_source$n_matches[4] = sum(!is.na(datGenes$gene_biotype)) - sum(labels_source$n_matches)
########################################################################################
# Plot results
labels_source = labels_source %>% mutate(x = 1, percentage = round(100*n_matches/sum(n_matches),1))
p = labels_source %>% ggplot(aes(x, percentage, fill=source)) + geom_bar(position = 'stack', stat = 'identity') +
theme_minimal() + coord_flip() + theme(legend.position='bottom', axis.title.y=element_blank(),
axis.text.y=element_blank(), axis.ticks.y=element_blank())
ggplotly(p + theme(legend.position='none'))as_ggplot(get_legend(p))
########################################################################################
# Reorder rows to match datExpr
datGenes = datGenes[match(rownames(datExpr), datGenes$ensembl_gene_id),]
rm(getinfo, mart, datGenes_biotype, datGenes_biotype_by_gene, datGenes_biotype_archive,
dups, missing_ensembl_ids, missing_genes, labels_source, p)Filtering
Checking how many SFARI genes are in the dataset
df = SFARI_genes %>% dplyr::select(-gene_biotype) %>% inner_join(datGenes, by=c('ID'='ensembl_gene_id'))
cat(paste0('Considering all genes, this dataset contains ', length(unique(df$`gene-symbol`)),
' of the ', length(unique(SFARI_genes$`gene-symbol`)), ' SFARI genes\n\n'))## Considering all genes, this dataset contains 979 of the 980 SFARI genescat(paste0('The missing gene is ',
SFARI_genes$`gene-symbol`[! SFARI_genes$`gene-symbol` %in% df$`gene-symbol`],
' with a SFARI score of ',
SFARI_genes$`gene-score`[! SFARI_genes$`gene-symbol` %in% df$`gene-symbol`]))## The missing gene is MIR137 with a SFARI score of 3rm(df)
1. Filter entries that don’t correspond to genes
to_keep = !is.na(datGenes$length)
cat(paste0('Names of the rows removed: ', paste(rownames(datExpr)[!to_keep], collapse=', ')))## Names of the rows removed: __no_feature, __ambiguous, __too_low_aQual, __not_aligned, __alignment_not_uniquedatGenes = datGenes[to_keep,]
datExpr = datExpr[to_keep,]
rownames(datGenes) = datGenes$ensembl_gene_id
cat(paste0('Removed ', sum(!to_keep), ' \'genes\', ', sum(to_keep), ' remaining'))## Removed 5 'genes', 63677 remaining
2. Filter genes that do not encode any protein
cat(paste0(sum(datGenes$gene_biotype=='protein_coding'), '/', nrow(datGenes), ' are protein coding genes' ))## 22543/63677 are protein coding genessort(table(datGenes$gene_biotype), decreasing=TRUE)##
## protein_coding lncRNA
## 22543 12167
## processed_pseudogene unprocessed_pseudogene
## 10117 2547
## 1 miRNA
## 2314 2276
## misc_RNA snRNA
## 2178 2043
## pseudogene snoRNA
## 1410 1202
## lincRNA transcribed_unprocessed_pseudogene
## 840 682
## rRNA_pseudogene transcribed_processed_pseudogene
## 500 441
## antisense 3
## 380 331
## 6 IG_V_pseudogene
## 314 254
## IG_V_gene TR_V_gene
## 179 146
## transcribed_unitary_pseudogene TR_J_gene
## 86 81
## unitary_pseudogene processed_transcript
## 74 72
## sense_intronic IG_D_gene
## 72 64
## rRNA TR_V_pseudogene
## 49 46
## sense_overlapping scaRNA
## 38 31
## polymorphic_pseudogene 7
## 28 25
## IG_J_gene IG_C_gene
## 24 23
## Mt_tRNA 4
## 22 17
## IG_C_pseudogene TEC
## 11 11
## TR_C_gene 3prime_overlapping_ncrna
## 8 6
## IG_J_pseudogene ribozyme
## 6 5
## TR_J_pseudogene TR_D_gene
## 5 3
## Mt_rRNA 8
## 2 1
## translated_processed_pseudogene translated_unprocessed_pseudogene
## 1 1
## vaultRNA
## 1Most of the genes with low expression levels are not protein-coding
plot_data = data.frame('ID' = rownames(datExpr), 'MeanExpr' = apply(datExpr, 1, mean),
'ProteinCoding' = datGenes$gene_biotype=='protein_coding')
ggplotly(plot_data %>% ggplot(aes(log2(MeanExpr+1), fill=ProteinCoding, color=ProteinCoding)) + geom_density(alpha=0.5) +
theme_minimal())rm(plot_data)We lose 3 genes with a SFARI score
Note: The gene name for Ensembl ID ENSG00000187951 is wrong, it should be AC091057.1 instead of ARHGAP11B, but the biotype is right, so it would still be filtered out
df = SFARI_genes %>% dplyr::select(-gene_biotype) %>% inner_join(datGenes, by=c('ID'='ensembl_gene_id'))
cat(paste0('Filtering protein coding genes, we are left with ', length(unique(df$`gene-symbol`[df$gene_biotype=='protein_coding'])),
' SFARI genes'))## Filtering protein coding genes, we are left with 976 SFARI geneskable(df %>% filter(! `gene-symbol` %in% df$`gene-symbol`[df$gene_biotype=='protein_coding']) %>%
dplyr::select(ID, `gene-symbol`, `gene-score`, gene_biotype, syndromic, `number-of-reports`), caption='Lost Genes')| ID | gene-symbol | gene-score | gene_biotype | syndromic | number-of-reports |
|---|---|---|---|---|---|
| ENSG00000187951 | ARHGAP11B | 4 | lncRNA | 0 | 2 |
| ENSG00000251593 | MSNP1AS | 2 | processed_pseudogene | 0 | 12 |
| ENSG00000197558 | SSPO | 4 | transcribed_unitary_pseudogene | 0 | 3 |
rm(df)if(!all(rownames(datExpr)==rownames(datGenes))) cat('!!! gene rownames do not match!!!')
to_keep = datGenes$gene_biotype=='protein_coding'
datExpr = datExpr %>% filter(to_keep)
datGenes = datGenes %>% filter(to_keep)
rownames(datExpr) = datGenes$ensembl_gene_id
rownames(datGenes) = datGenes$ensembl_gene_id
cat(paste0(length(unique(SFARI_genes$`gene-symbol`[SFARI_genes$ID %in% rownames(datExpr)])), ' SFARI genes remaining'))## 976 SFARI genes remainingcat(paste0('Removed ', sum(!to_keep), ' genes, ', sum(to_keep), ' remaining'))## Removed 41134 genes, 22543 remaining
3. Filter genes with low expression levels
\(\qquad\) 3.1 Remove genes with zero expression in all of the samples
to_keep = rowSums(datExpr)>0
cat(paste0('Removed ', sum(!to_keep), ' genes, ', sum(to_keep), ' remaining'))## Removed 3368 genes, 19175 remainingdf = data.frame('rowSums' = rowSums(datExpr), 'ensembl_gene_id' = rownames(datExpr)) %>%
right_join(SFARI_genes, by='ensembl_gene_id') %>% filter(rowSums==0 & `gene-score` %in% c(1,2,3)) %>%
arrange(`gene-score`) %>% dplyr::select(-ensembl_gene_id) %>% filter(!duplicated(`gene-symbol`))
kable(df %>% dplyr::select(ID, `gene-symbol`, `gene-score`, gene_biotype, syndromic, `number-of-reports`),
caption='Lost Genes with Top Scores')| ID | gene-symbol | gene-score | gene_biotype | syndromic | number-of-reports |
|---|---|---|---|---|---|
| ENSG00000267910 | KMT2A | 1 | protein_coding | 1 | 20 |
| ENSG00000227460 | SYNGAP1 | 1 | protein_coding | 1 | 50 |
| ENSG00000265594 | CEP41 | 2 | protein_coding | 0 | 5 |
| ENSG00000272883 | CNOT3 | 2 | protein_coding | 1 | 5 |
| ENSG00000259938 | CUX1 | 2 | protein_coding | 0 | 6 |
| ENSG00000271019 | MBOAT7 | 2 | protein_coding | 1 | 3 |
| ENSG00000268563 | MECP2 | 2 | protein_coding | 1 | 75 |
| ENSG00000262024 | TCF20 | 2 | protein_coding | 1 | 10 |
| ENSG00000266334 | APH1A | 3 | protein_coding | 0 | 2 |
| ENSG00000260508 | GGNBP2 | 3 | protein_coding | 0 | 2 |
| ENSG00000272706 | GRIK5 | 3 | protein_coding | 0 | 8 |
| ENSG00000269816 | KDM5C | 3 | protein_coding | 0 | 22 |
| ENSG00000235718 | MFRP | 3 | protein_coding | 0 | 6 |
| ENSG00000267946 | TMLHE | 3 | protein_coding | 0 | 5 |
| ENSG00000260150 | ZMYND11 | 3 | protein_coding | 0 | 10 |
datGenes = datGenes[to_keep,]
datExpr = datExpr[to_keep,]
cat(paste0(length(unique(SFARI_genes$`gene-symbol`[SFARI_genes$ID %in% rownames(datExpr)])), ' SFARI genes remaining'))## 970 SFARI genes remainingrm(df)\(\qquad\) 2.2 Removing genes with a high percentage of zeros
Choosing the threshold:
Criteria for selecting the percentage of zeros threshold: The minimum value in which the preprocessed data is relatively homoscedastic (we’re trying to get rid of the group of genes with very low mean and SD that make the cloud of points look like a comic book speech bubble)
On the plot I’m using the “dual” of the maximum percentage of zeros, the minimum percentage of non zeros so the visualisation is more intuitive
75% seems to be a good threshold for the minimum percentage of non zeros, so 25% will be the maximum percentage of zeros allowed in a row
The Mean vs SD plot doesn’t show all of the genes, a random sample was selected for the genes with higher level of expression so the visualisation wouldn’t be as heavy (and since we care about the genes with the lowest levels of expression, we aren’t losing important information)
datMeta_original = datMeta
datExpr_original = datExpr
datGenes_original = datGenes# Return to original variables
datExpr = datExpr_original
datGenes = datGenes_original
datMeta = datMeta_original
rm(datExpr_original, datGenes_original, datMeta_original, datExpr_vst, datGenes_vst, datMeta_vst)Filtering
# Minimum percentage of non-zero entries allowed per gene
threshold = 75
plot_data = data.frame('id'=rownames(datExpr), 'non_zero_percentage' = apply(datExpr, 1, function(x) 100*mean(x>0)))
ggplotly(plot_data %>% ggplot(aes(x=non_zero_percentage)) + geom_density(color='#0099cc', fill='#0099cc', alpha=0.3) +
geom_vline(xintercept=threshold, color='gray') + #scale_x_log10() +
ggtitle('Percentage of non-zero entries distribution') + theme_minimal())to_keep = apply(datExpr, 1, function(x) 100*mean(x>0)) >= threshold
datGenes = datGenes[to_keep,]
datExpr = datExpr[to_keep,]
cat(paste0(length(unique(SFARI_genes$`gene-symbol`[SFARI_genes$ID %in% rownames(datExpr)])), ' SFARI genes remaining'))## 924 SFARI genes remainingcat(paste0('Removed ', sum(!to_keep), ' genes, ', sum(to_keep), ' remaining'))## Removed 3028 genes, 16147 remainingrm(threshold, plot_data, to_keep)
3. Filter outlier samples
\(\qquad\) 3.1 Gandal filters samples belonging to subject AN03345 without giving an explanation. Since it could have some technical problems, I remove them as well
to_keep = (datMeta$Subject_ID != 'AN03345')
datMeta = datMeta[to_keep,]
datExpr = datExpr[,to_keep]
cat(paste0('Removed ', sum(!to_keep), ' samples, ', sum(to_keep), ' remaining'))## Removed 2 samples, 86 remaining\(\qquad\) 3.2 Filter out outliers: Using node connectivity as a distance measure, normalising it and filtering out genes farther away than 2 standard deviations from the left (lower connectivity than average, not higher)
Gandal uses the formula \(s_{ij}=\frac{1+bw(i,j)}{2}\) to convert all the weights to positive values, but I used \(s_{ij}=|bw(i,j)|\) instead because I think it makes more sense. In the end it doesn’t matter because they select as outliers the same six samples
All the outlier samples belong to the ASD group. Apart from this they don’t seem to have any characterstic in common (different subjects, extraction batches, brain lobes, age, PMI), except for Sex (but this is probably just because of the sex bias in the dataset)
absadj = datExpr %>% bicor %>% abs
netsummary = fundamentalNetworkConcepts(absadj)
ku = netsummary$Connectivity
z.ku = (ku-mean(ku))/sqrt(var(ku))
plot_data = data.frame('sample'=1:length(z.ku), 'distance'=z.ku, 'Sample_ID'=datMeta$Sample_ID,
'Subject_ID'=datMeta$Subject_ID, 'Extraction_Batch'=datMeta$RNAExtractionBatch,
'Brain_Lobe'=datMeta$Brain_lobe, 'Sex'=datMeta$Sex, 'Age'=datMeta$Age,
'Diagnosis'=datMeta$Diagnosis, 'PMI'=datMeta$PMI)
selectable_scatter_plot(plot_data, plot_data[,-c(1,2)])cat(paste0('Outlier samples: ', paste(as.character(plot_data$Sample_ID[plot_data$distance< -2]), collapse=', ')))## Outlier samples: AN01971_BA38, AN17254_BA17, AN09714_BA38, AN01093_BA7, AN02987_BA17, AN11796_BA7to_keep = z.ku > -2
datMeta = datMeta[to_keep,]
datExpr = datExpr[,to_keep]
cat(paste0('Removed ', sum(!to_keep), ' samples, ', sum(to_keep), ' remaining'))## Removed 6 samples, 80 remainingrm(absadj, netsummary, ku, z.ku, plot_data, to_keep)Imputing values
When trying different preprocessing pipelines we noticed that removing the gene ENSG00000187951 (ARHGAP11B) created an important change in the WGCNA modules, and when analysing what could have caused this, we found that this gene had a very big outlier value for a single sample, which we think altered the values of the other genes during the vst transformation enough to alter the modules by the WGNCA algorithm (See 20_02_28_comparison.html for more details about this).
Since the original preprocessing pipeline did not catch this behaviour and it ended up causing problems in downstream analyses, we decided to add a final step to identify problematic genes and fix this errors before they can cause any problems.
The metric be found to work best for detecting this genes is \(metric_i = max_j \frac{|x_{i,j} - mean(x_i)|}{sd(x_i)}\), where \(i \in \{1, ..., n\_genes}\) and \(j \in {1, ... , n\_samples}\), (using this metric, the problematic gene had the 4th largest value of all the dataset). More details about this metric can be found in 20_03_11_z_score_outlier_methods.html.
z_scores = datExpr %>% apply(1, function(x) abs(x-mean(x))/sd(x)) %>% t %>% data.frame
max_z_scores = data.frame('ID' = rownames(z_scores), 'max_z_score' = z_scores %>% apply(1, max),
'outlier_sample' = z_scores %>% apply(1, function(x) datMeta$Sample_ID[which.max(x)]))
rm(z_scores)Outiler sample detection
It was discovered that the maximum |z-score|s of the genes tend to group around a small set of samples, which suggests the sample is faulty instead of just the measurement for the genes that had their maximum |z-score| there.
The horizontal dotted line shows the limit to the number of outliers in a single sample that are inside three standard deviations of the mean, anything above this value is considered an outlier sample and will be eliminated from the dataset.
samples_info = max_z_scores$outlier_sample %>% table %>% data.frame %>%
dplyr::rename(Sample_ID = '.', Count = 'Freq') %>%
left_join(datMeta, by = 'Sample_ID') %>% arrange(desc(Count)) %>%
mutate('StandardisedCount' = round((Count-mean(Count))/sd(Count),2)) %>%
dplyr::select(Sample_ID, Count, StandardisedCount, Subject_ID, Sex, Age, Brain_lobe, Batch, Diagnosis)
ggplotly(samples_info %>% ggplot(aes(Subject_ID, Count, fill=Diagnosis, color=Brain_lobe)) +
geom_bar(stat = 'identity', size = 0, position = position_dodge2(preserve = 'single')) + xlab('Subjects') +
geom_hline(yintercept = nrow(max_z_scores)/nrow(datMeta), color = 'gray') +
geom_hline(yintercept = mean(samples_info$Count)+3*sd(samples_info$Count), color = 'gray', linetype = 'dotted') +
ggtitle('Number of genes for which their maximum |z-score| value fell in each Sample') +
theme_minimal() + theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = 'none'))outlier_samples = samples_info$Sample_ID[samples_info$StandardisedCount > 3]
if(length(outlier_samples)>0){
cat(paste0('Outlier sample(s): ', paste(outlier_samples, collapse = ', ')))
} else {
cat('There are no outlier samples that need to be removed')
}## Outlier sample(s): AN13872_BA4_6, AN02987_BA38# Remove faulty samples
datExpr = datExpr %>% dplyr::select(-paste0('X',datMeta$Dissected_Sample_ID[datMeta$Sample_ID %in% outlier_samples]))
datMeta = datMeta %>% dplyr::filter(!Sample_ID %in% outlier_samples)
cat(paste0('Removed ', length(outlier_samples), ' samples, ', nrow(datMeta), ' remaining'))## Removed 2 samples, 78 remainingrm(samples_info, outlier_samples)Value imputation
Recalculating max|z-score|s without the faulty samples
z_scores = datExpr %>% apply(1, function(x) abs(x-mean(x))/sd(x)) %>% t %>% data.frame
max_z_scores = data.frame('ID' = rownames(z_scores), 'max_z_score' = z_scores %>% apply(1, max),
'outlier_sample' = z_scores %>% apply(1, function(x) datMeta$Sample_ID[which.max(x)]),
'outlier_sample_dissected_id' = z_scores %>% apply(1, function(x) datMeta$Dissected_Sample_ID[which.max(x)])) %>%
mutate(norm_max_z_score = (max_z_score-mean(max_z_score))/sd(max_z_score),
has_outlier = max_z_score > mean(max_z_score) + 3*sd(max_z_score))
above_3_sd = mean(max_z_scores$max_z_score) + 3*sd(max_z_scores$max_z_score)
p = max_z_scores %>% ggplot(aes(ID, max_z_score, color=has_outlier)) + geom_point(alpha=0.2) +
geom_hline(yintercept = above_3_sd, color='gray', linetype = 'dotted') +
xlab('Genes') + ylab('Max |z-score|') + theme_minimal() +
ggtitle('Max|z-score| value for each gene') +
theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(),
panel.grid.major = element_blank())
ggExtra::ggMarginal(p, type = 'density', margins='y', fill='gray', color = 'gray', size=10)
cat(paste0(sum(max_z_scores$has_outlier), ' genes have outlier entries'))## 289 genes have outlier entriesrm(z_scores, above_3_sd, p)Genes with the highest outlier scores
Note: Plots are in logarithmic scale and the horizontal line is the median
plot_function = function(i){
i = 3*i-2
plot_list = list()
for(j in 1:3){
plot_data = data.frame('sample' = colnames(datExpr), 'expr' = t(datExpr[top_genes$ID[i+j-1],]),
'Diagnosis' = datMeta$Diagnosis)
colnames(plot_data)[2] = 'expr'
plot_list[[j]] = ggplotly(plot_data %>% ggplot(aes(sample, expr+1, color=Diagnosis)) +
geom_hline(yintercept = median(plot_data$expr)+1, color='gray') +
geom_point() + theme_minimal() + scale_y_log10() +
theme(legend.position='none', axis.text.x=element_blank(), axis.ticks.x=element_blank()))
}
p = subplot(plot_list, nrows=1) %>% layout(annotations = list(
list(x = 0.1 , y = 1.05, text = top_genes$hgnc_symbol[i], showarrow = F, xref='paper', yref='paper'),
list(x = 0.5 , y = 1.05, text = top_genes$hgnc_symbol[i+1], showarrow = F, xref='paper', yref='paper'),
list(x = 0.9 , y = 1.05, text = top_genes$hgnc_symbol[i+2], showarrow = F, xref='paper', yref='paper')))
return(p)
}
top_genes = max_z_scores %>% dplyr::top_n(n=21, w=max_z_score) %>% arrange(desc(max_z_score)) %>%
left_join(datGenes, by=c('ID' = 'ensembl_gene_id')) %>%
dplyr::select(ID, hgnc_symbol, max_z_score, norm_max_z_score)
plot_function(1)plot_function(2)plot_function(3)rm(plot_function, top_genes)Imputed value: The average expression level in that specific gene from the sample’s Diagnosis group (in case the gene has different expression levels by Diagnosis)
- Since the maximum value we just imputed could be hiding other smaller outlier values underneath it, after its value is imputed, the max|z-score| of the gene is recalculated, and if this value remains above the outlier threshold, the outlier entry is also imputed. This is repeated until there are no more outlier entries.
outliers = which(max_z_scores$has_outlier)
max_z_scores$imputed_values = 0
for(i in outliers){
max_score = abs(max(abs(datExpr[i,]-mean(datExpr[i,]))/sd(datExpr[i,]))-mean(max_z_scores$max_z_score))/sd(max_z_scores$max_z_score)
while(max_score > 3){
sample = gsub('X','',names(which.max(abs(datExpr[i,]-mean(datExpr[i,]))/sd(datExpr[i,]))))
diagnosis = datMeta$Diagnosis[datMeta$Dissected_Sample_ID == sample] %>% as.character
columns = datMeta$Dissected_Sample_ID[datMeta$Diagnosis == diagnosis & datMeta$Dissected_Sample_ID != sample]
datExpr[i,paste0('X',sample)] = datExpr[i, paste0('X',columns)] %>% mean %>% round
max_z_scores$imputed_values[i] = max_z_scores$imputed_values[i] + 1
# Prepare for next round
max_score = abs(max(abs(datExpr[i,]-mean(datExpr[i,]))/sd(datExpr[i,]))-mean(max_z_scores$max_z_score))/sd(max_z_scores$max_z_score)
}
}
cat('Number of imputed values in each gene')## Number of imputed values in each genetable(max_z_scores$imputed_values)##
## 0 1 2 3 4
## 15858 267 12 7 3rm(i, outliers, max_score, sample, diagnosis, columns)Samples that originally had the largest outlier values, after correction
plot_function = function(i){
i = 3*i-2
plot_list = list()
for(j in 1:3){
plot_data = data.frame('sample' = colnames(datExpr), 'expr' = t(datExpr[top_genes$ID[i+j-1],]),
'Diagnosis' = datMeta$Diagnosis)
colnames(plot_data)[2] = 'expr'
plot_list[[j]] = ggplotly(plot_data %>% ggplot(aes(sample, expr+1, color=Diagnosis)) +
geom_hline(yintercept = median(plot_data$expr)+1, color='gray') +
geom_point() + theme_minimal() + scale_y_log10() +
theme(legend.position='none', axis.text.x=element_blank(), axis.ticks.x=element_blank()))
}
p = subplot(plot_list, nrows=1) %>% layout(annotations = list(
list(x = 0.1 , y = 1.05, text = top_genes$hgnc_symbol[i], showarrow = F, xref='paper', yref='paper'),
list(x = 0.5 , y = 1.05, text = top_genes$hgnc_symbol[i+1], showarrow = F, xref='paper', yref='paper'),
list(x = 0.9 , y = 1.05, text = top_genes$hgnc_symbol[i+2], showarrow = F, xref='paper', yref='paper')))
return(p)
}
top_genes = max_z_scores %>% dplyr::top_n(n=21, w=max_z_score) %>% arrange(desc(max_z_score)) %>%
left_join(datGenes, by=c('ID' = 'ensembl_gene_id')) %>%
dplyr::select(ID, hgnc_symbol, max_z_score, norm_max_z_score)
plot_function(1)plot_function(2)plot_function(3)rm(plot_function, top_genes)cat(paste0('After filtering, the dataset consists of ', nrow(datExpr), ' genes and ', ncol(datExpr), ' samples'))## After filtering, the dataset consists of 16147 genes and 78 samplesBatch Effects
According to Tackling the widespread and critical impact of batch effects in high-throughput data, technical artifacts can be an important source of variability in the data, so batch correction should be part of the standard preprocessing pipeline of gene expression data.
They say Processing group and Date of the experiment are good batch surrogates, so I’m going to see if they affect the data in any clear way to use them as surrogates.
Processing group
All the information we have is the Brain Bank, and although all the samples were obtained from the Autism Tissue Project, we don’t have any more specific information about who preprocessed each sample
table(datMeta$Brain_Bank)##
## ATP
## 78Date of processing
There are two different dates when the data was procesed
table(datMeta$RNAExtractionBatch)##
## 10/10/2014 6/20/2014
## 52 26Luckily, there doesn’t seem to be a correlation between the batch surrogate and the objective variable, so the batch effect will not get confused with the Diagnosis effect
table(datMeta$RNAExtractionBatch, datMeta$Diagnosis)##
## CTL ASD
## 10/10/2014 24 28
## 6/20/2014 11 15*All the samples from each subject were processed on the same day
Samples don’t seem to cluster together that strongly for each batch, although there does seem to be some kind of relation
h_clusts = datExpr %>% t %>% dist %>% hclust %>% as.dendrogram
create_viridis_dict = function(){
min_age = datMeta$Age %>% min
max_age = datMeta$Age %>% max
viridis_age_cols = viridis(max_age - min_age + 1)
names(viridis_age_cols) = seq(min_age, max_age)
return(viridis_age_cols)
}
viridis_age_cols = create_viridis_dict()
dend_meta = datMeta[match(substring(labels(h_clusts),2), datMeta$Dissected_Sample_ID),] %>%
mutate('Batch' = ifelse(RNAExtractionBatch=='10/10/2014', '#F8766D', '#00BFC4'),
'Diagnosis' = ifelse(Diagnosis=='CTL','#008080','#86b300'), # Blue control, Green ASD
'Sex' = ifelse(Sex=='F','#ff6666','#008ae6'), # Pink Female, Blue Male
'Region' = case_when(Brain_lobe=='Frontal'~'#F8766D', # ggplot defaults for 4 colours
Brain_lobe=='Temporal'~'#7CAE00',
Brain_lobe=='Parietal'~'#00BFC4',
Brain_lobe=='Occipital'~'#C77CFF'),
'Age' = viridis_age_cols[as.character(Age)]) %>% # Purple: young, Yellow: old
dplyr::select(Age, Region, Sex, Diagnosis, Batch)
h_clusts %>% as.dendrogram %>% dendextend::set('labels', rep('', nrow(datMeta))) %>%
dendextend::set('branches_k_color', k=9) %>% plot
colored_bars(colors=dend_meta)
rm(h_clusts, dend_meta, create_viridis_dict, viridis_age_cols)Comparing the mean expression of each sample by batch we can see there is some batch effect differentiating them
plot_data_b1 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='10/10/2014']), 'Batch'='10/10/2014')
plot_data_b2 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='6/20/2014']), 'Batch'='6/20/2014')
plot_data = rbind(plot_data_b1, plot_data_b2)
mu = plot_data %>% group_by(Batch) %>% dplyr::summarise(BatchMean=mean(Mean))
ggplotly(plot_data %>% ggplot(aes(x=Mean, color=Batch, fill=Batch)) + geom_density(alpha=0.3) +
geom_vline(data=mu, aes(xintercept=BatchMean, color=Batch), linetype='dashed') +
ggtitle('Mean expression by sample grouped by Batch') + scale_x_log10() + theme_minimal())rm(plot_data_b1, plot_data_b2, plot_data, mu)Looking for unknown sources of batch effects
Following the pipeline from Surrogate variable analysis: hidden batch effects where sva is used with DESeq2.
Create a DeseqDataSet object, estimate the library size correction and save the normalized counts matrix
counts = datExpr %>% as.matrix
rowRanges = GRanges(datGenes$chromosome_name,
IRanges(datGenes$start_position, width=datGenes$length),
strand=datGenes$strand,
feature_id=datGenes$ensembl_gene_id)
se = SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=datMeta)
dds = DESeqDataSet(se, design = ~ Diagnosis)## converting counts to integer modedds = estimateSizeFactors(dds)
norm.cts = counts(dds, normalized=TRUE)Provide the normalized counts and two model matrices to SVA. The first matrix uses the biological condition, and the second model matrix is the null model.
mod = model.matrix(~ Diagnosis, colData(dds))
mod0 = model.matrix(~ 1, colData(dds))
sva_fit = svaseq(norm.cts, mod=mod, mod0=mod0)## Number of significant surrogate variables is: 13
## Iteration (out of 5 ):1 2 3 4 5rm(mod, mod0, norm.cts)Found 13 surrogate variables, since there is no direct way to select which ones to pick Bioconductor answer, kept all of them.
Include SV estimations to datMeta information
sv_data = sva_fit$sv %>% data.frame
colnames(sv_data) = paste0('SV',1:ncol(sv_data))
datMeta_sva = cbind(datMeta, sv_data)
rm(sv_data, sva_fit)In conclusion: Date of extraction works as a surrogate for batch effect and the sva package found other 13 variables that could work as surrogates which are now included in datMeta and should be included in the DEA.
Normalisation and Differential Expression Analysis
Using DESeq2 package to perform normalisation. I chose this package over limma because limma uses the log transformed data as input instead of the raw counts and I have discovered that in this dataset, this transformation affects genes differently depending on their mean expression level, and genes with a high SFARI score are specially affected by this.
plot_data = data.frame('ID'=rownames(datExpr), 'Mean'=rowMeans(datExpr), 'SD'=apply(datExpr,1,sd))
plot_data %>% ggplot(aes(Mean, SD)) + geom_point(color='#0099cc', alpha=0.1) + geom_abline(color='gray') +
scale_x_log10() + scale_y_log10() + theme_minimal()
rm(plot_data)Using vst instead of rlog to perform normalisation. Bioconductor question explaining differences between methods. Chose vst because a) it is much faster than rlog (it is recommended to use vst for samples larger than 50), and b) Michael Love (author of DESEq2) recommends using it over rlog
Including a log fold change threshold of 0 in the results formula \(H_0:lfc=0\) because setting any other log fold change seems arbitrary and we risk losing genes with a significant differential expression for genes with a higher fold change, but not necessarily as significant.
counts = datExpr %>% as.matrix
rowRanges = GRanges(datGenes$chromosome_name,
IRanges(datGenes$start_position, width=datGenes$length),
strand=datGenes$strand,
feature_id=datGenes$ensembl_gene_id)
se = SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=datMeta_sva)
dds = DESeqDataSet(se, design = ~ Batch + SV1 + SV2 + SV3 + SV4 + SV5 + SV6 + SV7 + SV8 + SV9 +
SV10 + SV11 + SV12 + SV13 + Diagnosis)## converting counts to integer mode# Perform DEA
dds = DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingDE_info = results(dds, lfcThreshold=0, altHypothesis='greaterAbs')
# Perform vst
vsd = vst(dds)
datExpr_vst = assay(vsd)
datMeta_vst = colData(vsd)
datGenes_vst = rowRanges(vsd)
rm(counts, rowRanges, se, vsd)Using the plotting function DESEq2’s manual proposes to study vst’s output it looks like the data could be homoscedastic
meanSdPlot(datExpr_vst, plot=FALSE)$gg + theme_minimal()
When plotting point by point it seems like the group of genes with the lowest values behave differently to the rest
plot_data = data.frame('ID'=rownames(datExpr_vst), 'Mean'=rowMeans(datExpr_vst), 'SD'=apply(datExpr_vst,1,sd))
plot_data %>% ggplot(aes(Mean, SD)) + geom_point(color='#0099cc', alpha=0.2) +
scale_x_log10() + scale_y_log10() + theme_minimal()
rm(plot_data)Save filtered and annotated dataset
*Could have done this since before
save(datExpr, datMeta, datGenes, file='./../Data/filtered_raw_data_imputed.RData')
#load('./../Data/filtered_raw_data.RData')Rename normalised datasets to continue working with these
datExpr = datExpr_vst
datMeta = datMeta_vst %>% data.frame
datGenes = datGenes_vst
cat(paste0(length(unique(SFARI_genes$`gene-symbol`[SFARI_genes$ID %in% rownames(datExpr)])), ' SFARI genes remaining'))## 924 SFARI genes remainingcat(paste0('After filtering, the dataset consists of ', nrow(datExpr), ' genes and ', ncol(datExpr), ' samples'))## After filtering, the dataset consists of 16147 genes and 78 samplesrm(datExpr_vst, datMeta_vst, datGenes_vst, datMeta_sva)Batch Effect Correction
By including the surrogate variables in the DESeq formula we only modelled the batch effects into the DEA, but we didn’t actually correct them from the data, for that we need to use ComBat (or other equivalent package) in the already normalised data
SVA surrogate variables
In some places they say you shouldn’t correct these effects on the data because you risk losing biological variation, in others they say you should because they introduce noise to the data. The only thing everyone agrees on is that you shouldn’t remove them before performing DEA but instead include them in the model.
Based on the conclusions from Practical impacts of genomic data “cleaning” on biological discovery using surrogate variable analysis it seems like it may be a good idea to remove the batch effects from the data and not only from the DE analysis:
Using SVA, ComBat or related tools can increase the power to identify specific signals in complex genomic datasets (they found “greatly sharpened global and gene-specific differential expression across treatment groups”)
But caution should be exercised to avoid removing biological signal of interest
We must be precise and deliberate in the design and analysis of experiments and the resulting data, and also mindful of the limitations we impose with our own perspective
Open data exploration is not possible after such supervised “cleaning”, because effects beyond those stipulated by the researcher may have been removed
Comparing data with and without surrogate variable correction
# Taken from https://www.biostars.org/p/121489/#121500
correctDatExpr = function(datExpr, mod, svs) {
X = cbind(mod, svs)
Hat = solve(t(X) %*% X) %*% t(X)
beta = (Hat %*% t(datExpr))
rm(Hat)
gc()
P = ncol(mod)
return(datExpr - t(as.matrix(X[,-c(1:P)]) %*% beta[-c(1:P),]))
}
pca_samples_before = datExpr %>% t %>% prcomp
pca_genes_before = datExpr %>% prcomp
# Correct
mod = model.matrix(~ Diagnosis, colData(dds))
svs = datMeta %>% dplyr::select(SV1:SV13) %>% as.matrix
datExpr_corrected = correctDatExpr(as.matrix(datExpr), mod, svs)
pca_samples_after = datExpr_corrected %>% t %>% prcomp
pca_genes_after = datExpr_corrected %>% prcomp
rm(correctDatExpr)Samples
Removing batch effects has a big impact in the distribution of the samples, separating them by diagnosis almost perfectly just using the first principal component
pca_samples_df = rbind(data.frame('ID'=colnames(datExpr), 'PC1'=pca_samples_before$x[,1],
'PC2'=pca_samples_before$x[,2], 'corrected'=0),
data.frame('ID'=colnames(datExpr), 'PC1'=pca_samples_after$x[,1],
'PC2'=pca_samples_after$x[,2], 'corrected'=1)) %>%
left_join(datMeta %>% mutate('ID'=rownames(datMeta)), by='ID')
ggplotly(pca_samples_df %>% ggplot(aes(PC1, PC2, color=Diagnosis)) + geom_point(aes(frame=corrected, id=ID), alpha=0.75) +
xlab(paste0('PC1 (corr=', round(cor(pca_samples_before$x[,1],pca_samples_after$x[,1]),2),
'). % Var explained: ', round(100*summary(pca_samples_before)$importance[2,1],1),' to ',
round(100*summary(pca_samples_after)$importance[2,1],1))) +
ylab(paste0('PC2 (corr=', round(cor(pca_samples_before$x[,2],pca_samples_after$x[,2]),2),
'). % Var explained: ', round(100*summary(pca_samples_before)$importance[2,2],1),' to ',
round(100*summary(pca_samples_after)$importance[2,2],1))) +
ggtitle('Samples') + theme_minimal())rm(pca_samples_df)Genes
It seems like the sva correction preserves the mean expression of the genes and erases almost everything else (although what little else remains is enough to characterise the two Diagnosis groups pretty well using only the first PC)
*Plot is done with only 10% of the genes so it’s not that heavy
pca_genes_df = rbind(data.frame('ID'=rownames(datExpr), 'PC1'=pca_genes_before$x[,1],
'PC2'=pca_genes_before$x[,2], 'corrected'=0, 'MeanExpr'=rowMeans(datExpr)),
data.frame('ID'=rownames(datExpr), 'PC1'=pca_genes_after$x[,1],
'PC2'=pca_genes_after$x[,2], 'corrected'=1, 'MeanExpr'=rowMeans(datExpr)))
keep_genes = rownames(datExpr) %>% sample(0.1*nrow(datExpr))
pca_genes_df = pca_genes_df %>% filter(ID %in% keep_genes)
ggplotly(pca_genes_df %>% ggplot(aes(PC1, PC2,color=MeanExpr)) + geom_point(alpha=0.3, aes(frame=corrected, id=ID)) +
xlab(paste0('PC1 (corr=', round(cor(pca_genes_before$x[,1],pca_genes_after$x[,1]),2),
'). % Var explained: ', round(100*summary(pca_genes_before)$importance[2,1],1),' to ',
round(100*summary(pca_genes_after)$importance[2,1],1))) +
ylab(paste0('PC2 (corr=', round(cor(pca_genes_before$x[,2],pca_genes_after$x[,2]),2),
'). % Var explained: ', round(100*summary(pca_genes_before)$importance[2,2],1),' to ',
round(100*summary(pca_genes_after)$importance[2,2],1))) +
scale_color_viridis() + ggtitle('Genes') + theme_minimal())rm(pca_samples_before, pca_genes_before, mod, svs, pca_samples_after, pca_genes_after, pca_genes_df, keep_genes)Everything looks good, so we’re keeping the corrected expression dataset
datExpr = datExpr_corrected
rm(datExpr_corrected)Processing date
Even after correcting the dataset for the surrogate variables found with sva, there is still a difference in mean expression by processing date (although this difference is relatively small)
plot_data_b1 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='10/10/2014']), 'Batch'='10/10/2014')
plot_data_b2 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='6/20/2014']), 'Batch'='6/20/2014')
plot_data = rbind(plot_data_b1, plot_data_b2)
mu = plot_data %>% group_by(Batch) %>% dplyr::summarise(BatchMean=mean(Mean))
ggplotly(plot_data %>% ggplot(aes(x=Mean, color=Batch, fill=Batch)) + geom_density(alpha=0.3) +
geom_vline(data=mu, aes(xintercept=BatchMean, color=Batch), linetype='dashed') +
ggtitle('Mean expression by sample grouped by processing date') + scale_x_log10() + theme_minimal())rm(plot_data_b1, plot_data_b2, plot_data, mu)Performing Batch Correction for processing date
https://support.bioconductor.org/p/50983/
datExpr = datExpr %>% as.matrix %>% ComBat(batch=datMeta$Batch)## Found2batches## Adjusting for0covariate(s) or covariate level(s)## Standardizing Data across genes## Fitting L/S model and finding priors## Finding parametric adjustments## Adjusting the DataNow both batches have the same mean expression
plot_data_b1 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='10/10/2014']), 'Batch'='10/10/2014')
plot_data_b2 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='6/20/2014']), 'Batch'='6/20/2014')
plot_data = rbind(plot_data_b1, plot_data_b2)
mu = plot_data %>% group_by(Batch) %>% dplyr::summarise(BatchMean=mean(Mean))
ggplotly(plot_data %>% ggplot(aes(x=Mean, color=Batch, fill=Batch)) + geom_density(alpha=0.3) +
geom_vline(data=mu, aes(xintercept=BatchMean, color=Batch), linetype='dashed') +
ggtitle('Mean expression by sample grouped by processing date') + scale_x_log10() + theme_minimal())rm(plot_data_b1, plot_data_b2, plot_data, mu)Save preprocessed dataset
save(datExpr, datMeta, datGenes, DE_info, dds, file='./../Data/preprocessed_data_imputed.RData')
#load('./../Data/preprocessed_data.RData')Session info
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] knitr_1.28 expss_0.10.2
## [3] dendextend_1.13.4 vsn_3.52.0
## [5] WGCNA_1.69 fastcluster_1.1.25
## [7] dynamicTreeCut_1.63-1 sva_3.32.1
## [9] genefilter_1.66.0 mgcv_1.8-31
## [11] nlme_3.1-144 DESeq2_1.24.0
## [13] SummarizedExperiment_1.14.1 DelayedArray_0.10.0
## [15] BiocParallel_1.18.1 matrixStats_0.56.0
## [17] Biobase_2.44.0 GenomicRanges_1.36.1
## [19] GenomeInfoDb_1.20.0 IRanges_2.18.3
## [21] S4Vectors_0.22.1 BiocGenerics_0.30.0
## [23] biomaRt_2.40.5 ggpubr_0.2.5
## [25] magrittr_1.5 ggExtra_0.9
## [27] GGally_1.5.0 gridExtra_2.3
## [29] viridis_0.5.1 viridisLite_0.3.0
## [31] RColorBrewer_1.1-2 plotlyutils_0.0.0.9000
## [33] plotly_4.9.2 glue_1.3.2
## [35] reshape2_1.4.3 forcats_0.5.0
## [37] stringr_1.4.0 dplyr_0.8.5
## [39] purrr_0.3.3 readr_1.3.1
## [41] tidyr_1.0.2 tibble_3.0.0
## [43] ggplot2_3.3.0 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5 Hmisc_4.4-0
## [4] plyr_1.8.6 lazyeval_0.2.2 splines_3.6.3
## [7] crosstalk_1.1.0.1 digest_0.6.25 foreach_1.5.0
## [10] htmltools_0.4.0 GO.db_3.8.2 fansi_0.4.1
## [13] checkmate_2.0.0 memoise_1.1.0 doParallel_1.0.15
## [16] cluster_2.1.0 limma_3.40.6 annotate_1.62.0
## [19] modelr_0.1.6 prettyunits_1.1.1 jpeg_0.1-8.1
## [22] colorspace_1.4-1 blob_1.2.1 rvest_0.3.5
## [25] haven_2.2.0 xfun_0.12 hexbin_1.28.1
## [28] crayon_1.3.4 RCurl_1.98-1.1 jsonlite_1.6.1
## [31] impute_1.58.0 iterators_1.0.12 survival_3.1-11
## [34] gtable_0.3.0 zlibbioc_1.30.0 XVector_0.24.0
## [37] scales_1.1.0 DBI_1.1.0 miniUI_0.1.1.1
## [40] Rcpp_1.0.4 xtable_1.8-4 progress_1.2.2
## [43] htmlTable_1.13.3 foreign_0.8-75 bit_1.1-15.2
## [46] preprocessCore_1.46.0 Formula_1.2-3 htmlwidgets_1.5.1
## [49] httr_1.4.1 acepack_1.4.1 ellipsis_0.3.0
## [52] farver_2.0.3 pkgconfig_2.0.3 reshape_0.8.8
## [55] XML_3.99-0.3 nnet_7.3-13 dbplyr_1.4.2
## [58] locfit_1.5-9.4 labeling_0.3 tidyselect_1.0.0
## [61] rlang_0.4.5 later_1.0.0 AnnotationDbi_1.46.1
## [64] munsell_0.5.0 cellranger_1.1.0 tools_3.6.3
## [67] cli_2.0.2 generics_0.0.2 RSQLite_2.2.0
## [70] broom_0.5.5 evaluate_0.14 fastmap_1.0.1
## [73] yaml_2.2.1 bit64_0.9-7 fs_1.4.0
## [76] mime_0.9 xml2_1.2.5 compiler_3.6.3
## [79] rstudioapi_0.11 curl_4.3 png_0.1-7
## [82] affyio_1.54.0 ggsignif_0.6.0 reprex_0.3.0
## [85] geneplotter_1.62.0 stringi_1.4.6 highr_0.8
## [88] lattice_0.20-40 Matrix_1.2-18 vctrs_0.2.4
## [91] pillar_1.4.3 lifecycle_0.2.0 BiocManager_1.30.10
## [94] cowplot_1.0.0 data.table_1.12.8 bitops_1.0-6
## [97] httpuv_1.5.2 affy_1.62.0 R6_2.4.1
## [100] latticeExtra_0.6-29 promises_1.1.0 codetools_0.2-16
## [103] assertthat_0.2.1 withr_2.1.2 GenomeInfoDbData_1.2.1
## [106] hms_0.5.3 grid_3.6.3 rpart_4.1-15
## [109] rmarkdown_2.1 shiny_1.4.0.2 lubridate_1.7.4
## [112] base64enc_0.1-3